The parameters \(\theta \)  of the neural net policy are initialized by the Xavier initialization method proposed in [1]}. In the first iteration, the reference trajectory is obtained by the straight-line interpolation [2]}. Since having the penalty with the randomly initialized neural net policy in the first iteration can slow down the entire process, the convex sub-problem in (REF ) uses the trust region penalty with the input \(\bar{u}\)  from the reference trajectory instead of the input from the policy \(\pi _{\theta }(\bar{x})\)  in (REF ). Then, the convex sub-problem's solution provides a new reference trajectory for the next iteration, and the trust region penalty with the policy is employed from the second iteration.
